Hyperspectral identification of rice diseases and pests based on principal component analysis and probabilistic neural network

Correct and fast identification of rice diseases and pests was the basis of diseases and pests prevention measures, and significant in disaster assessment. This study adopted spectral reflectance of rice leaves stressed by rice Aphelenchoides besseyi Christie of two periods at the rice booting stage and by rice leaf roller of two periods at the rice tillering stage. With the analysis of the spectral characteristics of rice leaves, visible band (490-670 nm) and short wave infrared band (1 520-1 750 nm) were selected. The principal components spectrum were obtained with principal component analysis (PCA) transformed from the above two selected band. The recognition precision of rice Aphelenchoides besseyi Christie and rice leaf roller using probabilistic neural network (PNN) was as high as 95.65%. The research demonstrated that the method was feasible and reliable to precisely identify non-healthy rice stressed by rice diseases and pests from healthy rice based on PCA and PNN.